Textbooks

Required

This course makes use of several textbooks. I have attempted when possible to choose resources which are freely available.

1. Stephen Boyd and Lieven Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

This book provides a strong practical introduction to Linear Algebra. We are primarily using the later parts of the book to learn about applications of least squares. However, you are encouraged to use earlier parts of the book to review linear algebra if needed.

2. Lieven Vandenberghe and Stephen Boyd. Convex Optimization

This is an excellent book on practical applications of convex optimization. We will use the first half of the book, which focuses on identifying convex optimization problems.

3. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning

This is a good introduction to neural networks and deep learning.

Optional

1. Yang Xin-She. Introduction to algorithms for data mining and machine learning. Academic press, 2019.

This textbook provides a concise introduction to optimization for machine learning.

2. David Mackay Information Theory, Inference, and Learning Algorithms

This is free textbook covers a large number of topics that are of relevance to data science, often from a slightly different perspective than standard treatments. It provides an excellent introduction to Bayesian Inference and Neural Networks, but applies ideas from information theory too.